Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Automatic online news monitoring and classification for syndromic surveillance
Decision Support Systems
International Journal of Approximate Reasoning
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
ACM Transactions on Management Information Systems (TMIS)
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Journal of Biomedical Informatics
MaskedPainter: Feature selection for microarray data analysis
Intelligent Data Analysis
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High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection.